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Volume 2 | 2026

Concepts and solutions

Concept stage

  • The expanding attack surface of connected vehicles, coupled with increasingly stringent data protection regulations, demands a fundamental rethink of automotive cybersecurity. As threats evolve and compliance requirements tighten, conventional security based on isolation or measures on CAN/Ethernet bus systems is no longer sufficient.

     

    In this context, the principles of zero-trust architecture (ZTA) are essential for SDVs. ZTA's core tenet — "never trust, always verify" — allows organizations to strengthen their defenses in the evolving threat landscape. By enforcing strict access controls, employing robust encryption and implementing continuous, real-time monitoring, ZTA ensures every entity accessing the vehicle's network or resources is rigorously authenticated and authorized. This containment strategy guarantees that a compromise in one component doesn't jeopardize the entire vehicle's operation or sensitive data.

     

    We continue to observe that ZTA should be a foundational approach for creating resilient security in SDVs. It enables compliance with industry standards such as ISO/SAE 21434, which mandate strong security measures, including continuous verification and stringent access controls, to mitigate modern cyber threats and safeguard critical systems and user data.

  • Specialized foundational models are emerging to address distinct automotive tasks, moving beyond general-purpose large language models (LLMs). For example, vision LLMs (VLLMs) excel at object recognition, while audio LLMs (ALLMs) optimize voice recognition, each offering unique advantages for specific in-vehicle functions.

     

    As automotive organizations look to leverage these specialized models for efficiency and differentiation, a critical challenge arises: evaluating which fine-tuned model performs best for a company's specific domain task and proprietary data. Selecting the optimal model becomes a key strategic differentiator.

     

    Managed post training (MPT) directly addresses this need. It provides a managed service that enables automotive companies to rapidly evaluate multiple fine-tuned foundational models against their unique tasks and data. This makes it faster and easier to identify the most effective model, streamlining deployment and maximizing the value of specialized AI in the vehicle. And, once the task fit of a foundational model is established in a short iteration, the setup can be used for AI and ML loops that deliver updated models into production.

  • The microcontroller parallel processing unit (PPU) is emerging as a significant innovation for efficient edge AI deployment. This dedicated hardware is specifically designed to offload AI inference tasks traditionally handled by power-hungry GPUs or other accelerators.

     

    The core value of the PPU is its ability to execute real-time inferencing locally sitting beside the microcontroller. By doing so, it dramatically reduces the power consumption and hardware costs associated with more expensive accelerator solutions, while maintaining the performance levels necessary for specialized AI tasks. This shift makes scalable, cost-effective AI at the edge a tangible reality.

     

    This efficiency is particularly critical for automotive applications, where low-latency response and minimal energy usage are paramount for functions such as intelligent sensors and real-time inferences.

  • Passenger vehicles today can have over 100 ECUs, from microcontroller units (MCUs) powering basic functions like headlights to complex multiprocessor units (MPUs) running infotainment systems. To keep SDVs manageable, the number of ECUs must be controlled and, where possible, consolidated.

     

    A key enabler of ECU consolidation is advanced silicon technologies such as chiplets. Chiplets have lowered chip design costs, accelerated time-to-market, improved production yields, and integrated substantial functionality and processing power onto single chip packages. 

     

    However, consolidation removes the natural isolation between ECU software components running independently on separate processors and communicating through networks such as CAN and Ethernet. Historically, each ECU software component was designed to meet specific requirements, such as safety standards. With consolidation, heterogeneous software stacks now share common multiprocessor systems.

     

    Mixed criticality orchestrators (MCOs) can help OEMs enable ECU consolidation by ensuring there’s no interference between tasks. For example, MCOs can allow safety-critical tasks to run with sufficient memory and CPU resources as if they have sole occupancy of the system. MCOs can also manage multiple execution environments, including vehicle systems, edge computing in mobile networks with low latency, and remote cloud resources.

     

    Critically, MCOs require rigorous certification and validation to ensure they meet the safety and reliability requirements demanded by the automotive industry.

Early adoption stage

  • Multiple OEMs now offer portals to help developers build applications using vehicle APIs. The portals can include vehicle status information such as location, charging status or errors. Some offer the ability to send data and commands to vehicles, enabling use cases such as drive experience personalization, customized patterns for locking and unlocking, anti-kinetosis, charging pattern optimization or sleep detection.   

     

    These developer portals are key enablers for SDV ecosystem growth, providing the foundation for third-party applications that integrate seamlessly with vehicle systems. They support innovative use cases ranging from fleet management and smart home integration to personalized vehicle experiences and connected services. The portals also create opportunities for developers to build applications that use vehicle data and control capabilities while maintaining security and safety standards.

     

    The developer experience and functionality vary significantly across OEM portals, reflecting the early stage of this technology's evolution. Some portals offer comprehensive APIs with extensive documentation and testing tools, while others provide more limited access with basic functionality. This variation suggests the industry is still defining best practices for vehicle API design and developer engagement.

     

    The growing availability of vehicle APIs through developer portals will accelerate innovation in the automotive ecosystem by enabling a broader range of developers to contribute to vehicle functionality. This approach will also help OEMs differentiate their vehicles through unique applications and services while maintaining control over critical vehicle systems. As the automotive industry evolves, developer portals will become essential for building vibrant software ecosystems around vehicles.

  • The adoption of virtual ECUs (vECUs) for development continues to advance, enabling earlier integration and collaboration. OEMs are increasingly integrating multiple vECUs early in the software development lifecycle. Architectures now also interconnect supplier-owned vECU environments, allowing independent software deployment, faster iteration and earlier error detection when fixes are less costly.

     

    However, OEMs’ expectations for vECUs must take into account that, while some cases achieve binary compatibility with hardware, the significant investment required to create exact replicas of complex ECUs often yields diminishing returns. The highest value lies in using vECUs to uncover integration issues early, analyze system behavior and run parallel simulations of vehicle instances with different software variants to accelerate validation cycles.

     

    While vECU technology shows clear improvement and enables valuable workflows, a fully synchronized, hardware-identical virtual network — particularly concerning CPU clock speed synchronization for complex multi-vECU testing — remains a challenge. Focusing on high-value use cases, rather than exhaustive replication, offers the most pragmatic way to maximize vECU benefits.

  • Multi-tenant compute is a computing architecture where multiple, independent applications, services or virtual machines share the same physical hardware resources while maintaining strict isolation and security boundaries. In automotive contexts, this means running vehicle systems — such as infotainment, Advanced Driver Assistance Systems (ADAS), telematics and third-party applications — on shared computing platforms rather than dedicated hardware for each function.

     

    The need for multi-tenant compute has emerged from the industry's shift toward new vehicle architectures. Traditional distributed ECU architectures are being replaced by zonal and central computing architectures that consolidate multiple functions onto shared platforms. This requires sophisticated resource management and isolation mechanisms to ensure safety-critical systems, infotainment applications and third-party services can coexist securely on the same hardware.

     

    Multi-tenant operating systems like seL4 and L4Re provide the foundation for this architecture, offering formal verification of security properties and microkernel-based isolation. These systems enable OEMs to run applications from different suppliers, third-party services and internal systems on the same hardware with guaranteed security boundaries. They also provide resource management for efficient allocation of computing resources based on real-time demands.

  • A vehicle abstraction layer (VAL) provides a higher-level interface to access a vehicle’s functionality without having detailed knowledge about the underlying implementation, sensors and actors. 

     

    Unlike the traditional approach of pre-planned, signal-based communication between components, a VAL abstracts a vehicle’s capabilities, separating hardware-bound software from features software. This allows more flexibility for additional features provided by application updates, supports the porting of features to different vehicle models, and is essential for external solution providers such as ADAS and AD, infotainment systems or connected mobility services.

     

    Many OEMs include the VAL concept in their internal architecture, but there are also more public examples that include this concept, such as Android Automotive, Veecle.io and the Vehicle Information Service Specification (VISS).

     

    While there are clear benefits to a VAL concept, it also comes with challenges, including difficulty supporting different service requirements. Some applications using the VAL need a response or execution done in real time, while others don’t, and implementing this mixed criticality is complex. 

     

    Another challenge we observe in VAL design is ‘leaky’ abstractions, where API layers are implicitly coupled to hardware. This results in brittle architectures, sometimes with multiple layers of APIs, so even simple feature additions require widespread ‘shotgun surgery’ instead of isolated changes.

Mass adoption stage

  • Virtual electronic control units (vECUs) have become foundational for modern automotive software development, enabling the design, testing and validation of embedded systems without waiting for physical hardware. This approach enables teams to rapidly develop and test new features and validate complex functionalities such as ADAS, significantly reducing development time and cost.

     

    vECUs reduce reliance on physical prototypes, enable the early detection of software problems and provide enhanced simulation capabilities for comprehensive system testing. They’re now integrated seamlessly into enterprise-grade CI/CD pipelines and cloud-based virtual engineering workbenches. 

     

    The simulation fidelity of vECUs has also significantly improved, and they’re now used deeper into the validation cycle, not just for early-stage testing. The explosion of data generated by vECU simulations is being tackled with unified logging, traceability and data lake architectures, enabling search, comparison and compliance reporting.

  • In recent years, OEMs have moved from distributed to domain-centric to centralized architectures containing a single or few high-performance computers (HPCs). The next phase of this evolution of electrical/electronic (EE) architectures is a hybrid approach combining centralization with an additional layer focused on the physical distribution of components.

     

    In this architecture, zonal control units (ZCUs) are strategically positioned throughout the vehicle, such as in the left, right, rear and cockpit zones. Within each designated zone, all local sensors, actuators and power consumers establish direct connections to their nearest ZCU. In turn, these ZCUs are connected to one or more central HPCs.

     

    This approach should lead to a substantial reduction in physical wiring, lowering the cost and weight of vehicles. Further potential benefits or downsides rely heavily on the amount of logic put into the ZCUs. For example, I/O aggregation, filtering and localized processing within the ZCUs allows for further abstracted communication to the HPCs. This can separate feature logic from the underlying hardware, but it could also lead to familiar challenges inherent in developing and managing complex, distributed systems.

Concepts and solutions

 

Capabilities available on the market as packaged solutions that SDV manufacturers can apply within vehicles or use to transform their internal operations.

Ecosystems and organizations

 

Emerging organizations and ecosystems that should be on every SDV manufacturer’s radar, including networks to be part of and regulatory bodies that could impact operations and engineering decisions.

Techniques and practices

 

New ways of working that can help SDV manufacturers evolve how their teams operate and enable them to deliver better results and driver outcomes.

Technology and products

 

Leading technologies SDV engineers and manufacturers can incorporate into vehicles or apply in their engineering organizations to transform experiences and deliver new value.

Trends

 

Relevant trends that don’t fall into the other four areas. Many of these are general evolutions in software engineering that SDV manufacturers should be aware of.